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In recent years, there has been widespread attention drawn to convolutionalneural network (CNN) based blind image quality assessment (IQA). A large numberof works start by extracting deep features from CNN. Then, those features areprocessed through spatial average pooling (SAP) and fully connected layers topredict quality. Inspired by full reference IQA and texture features, in thispaper, we extend SAP ($1^{st}$ moment) into spatial moment pooling (SMP) byincorporating higher order moments (such as variance, skewness). Moreover, weprovide learning friendly normalization to circumvent numerical issue whencomputing gradients of higher moments. Experimental results suggest that simplyupgrading SAP to SMP significantly enhances CNN-based blind IQA methods andachieves state of the art performance.

Spatial Moment Pooling Improves Neural Image Assessment